full transcript

From the Ted Talk by Nina Klietsch: Why do airlines sell too many tickets?

Unscramble the Blue Letters

Have you ever sat in a doctor's office for hours despite having an appointment at a specific time, has the hotel turned down your reservation because it's full? Or have you been bumped off a flight that you paid for? These are all symptoms of overbooking, a ptrcciae where bueissness and iioinsnttuts sell or book more than their full capacity. While often infuriating for the customer, overbooking happens because it increases profits while also letting businesses optizmie their resources. They know that not everyone will show up to their appointments, reservations and ftilhgs, so they make more available than they actually have to offer. Airlines are the classical example, partially because it happens so often, about 50000 poelpe get bumped off their flights each year. That figure comes at little surprise to the airlines themselves, which used ssatiittcs to determine exactly how many tickets to sell. It's a delicate operation, sell too few and they're wasting seats, sell too many and they pay penltieas, money, free flights, hotel stays and ayonned customers. So here's a sepilmfiid viosern of how their clatcolnaius work. Airlines have collected years worth of information about who does and doesn't show up for certain flights. They know, for example, that on a particular route, the probability that each individual customer will show up on time is 90 percent. For the sake of simplicity, will assume that every customer is tnlavierg individually rather than as families or groups, then if there are 180 seats on the plane and they sell 180 tickets, the most likely result is that 162 passengers will borad. But of course, you could also end up with more passengers or fewer. The prbblitaioy for each value is given by what's called a binomial distribution, which peaks at the most likely ocoutme. Now let's look at the revenue. The airline makes money from each ticket buyer and loses money for each person who gets bumped. Let's say a ticket costs 250 dollars and isn't exchangeable for a later flight and the cost of bumping a passenger is 800 dollars. These numbers are just for the sake of example. Actual amounts vary considerably. So here, if you don't sell any extra tickets, you make 45000 dollars. If you sell 15 extras and at least 15 people are no shows, you make forty eight tnaoushd seven hundred fifty dollars. That's the best case. In the wosrt case, everyone shows up, 15 unlucky passengers get bumped and the revenue will only be thirty six thousand seven hundred fifty dollars, even less than if you only sold 180 tickets in the first plcae. But what matters isn't just how good or bad a scenario is financially, but how likely it is to happen. So how likely is each scenario? We can find out by using the binomial distribution in this example, the probability of exactly 195 passengers boarding is almost zero percent. The probability of exactly 184 passengers boarding is one point one one percent and so on. Multiply these probabilities by the revenue for each case, add them all up and subtract the sum from the earnings by 195 sold tickets and you get the expected rvuenee for selling 195 tkteics. By repeating this calculation for various numbers of extra tickets, the airline can find the one likely to yield the highest revenue in this example. That's 198 tickets from which the aiinlre will probably make forty eight thousand seven hundred setenvy four dallors, almost 4000 more than without okoirevnbog. And that's just for one flight. Multiply that by a moliiln flights per airline per year. And overbooking adds up fast. Of course, the actual calculation is much more complicated airlines apply many factors to create even more accurate models, but should they? Some agrue that overbooking is unethical. You're charging two people for the same resource. Of course, if you're 100 percent sure someone won't show up, it's fine to sell their seat. But what if you're only 95 percent sure, 75 pcrenet. Is there a number that separates being uiaenchtl from being pacicrtal?

Open Cloze

Have you ever sat in a doctor's office for hours despite having an appointment at a specific time, has the hotel turned down your reservation because it's full? Or have you been bumped off a flight that you paid for? These are all symptoms of overbooking, a ________ where __________ and ____________ sell or book more than their full capacity. While often infuriating for the customer, overbooking happens because it increases profits while also letting businesses ________ their resources. They know that not everyone will show up to their appointments, reservations and _______, so they make more available than they actually have to offer. Airlines are the classical example, partially because it happens so often, about 50000 ______ get bumped off their flights each year. That figure comes at little surprise to the airlines themselves, which used __________ to determine exactly how many tickets to sell. It's a delicate operation, sell too few and they're wasting seats, sell too many and they pay _________, money, free flights, hotel stays and _______ customers. So here's a __________ _______ of how their ____________ work. Airlines have collected years worth of information about who does and doesn't show up for certain flights. They know, for example, that on a particular route, the probability that each individual customer will show up on time is 90 percent. For the sake of simplicity, will assume that every customer is _________ individually rather than as families or groups, then if there are 180 seats on the plane and they sell 180 tickets, the most likely result is that 162 passengers will _____. But of course, you could also end up with more passengers or fewer. The ___________ for each value is given by what's called a binomial distribution, which peaks at the most likely _______. Now let's look at the revenue. The airline makes money from each ticket buyer and loses money for each person who gets bumped. Let's say a ticket costs 250 dollars and isn't exchangeable for a later flight and the cost of bumping a passenger is 800 dollars. These numbers are just for the sake of example. Actual amounts vary considerably. So here, if you don't sell any extra tickets, you make 45000 dollars. If you sell 15 extras and at least 15 people are no shows, you make forty eight ________ seven hundred fifty dollars. That's the best case. In the _____ case, everyone shows up, 15 unlucky passengers get bumped and the revenue will only be thirty six thousand seven hundred fifty dollars, even less than if you only sold 180 tickets in the first _____. But what matters isn't just how good or bad a scenario is financially, but how likely it is to happen. So how likely is each scenario? We can find out by using the binomial distribution in this example, the probability of exactly 195 passengers boarding is almost zero percent. The probability of exactly 184 passengers boarding is one point one one percent and so on. Multiply these probabilities by the revenue for each case, add them all up and subtract the sum from the earnings by 195 sold tickets and you get the expected _______ for selling 195 _______. By repeating this calculation for various numbers of extra tickets, the airline can find the one likely to yield the highest revenue in this example. That's 198 tickets from which the _______ will probably make forty eight thousand seven hundred _______ four _______, almost 4000 more than without ___________. And that's just for one flight. Multiply that by a _______ flights per airline per year. And overbooking adds up fast. Of course, the actual calculation is much more complicated airlines apply many factors to create even more accurate models, but should they? Some _____ that overbooking is unethical. You're charging two people for the same resource. Of course, if you're 100 percent sure someone won't show up, it's fine to sell their seat. But what if you're only 95 percent sure, 75 _______. Is there a number that separates being _________ from being _________?

Solution

  1. overbooking
  2. outcome
  3. revenue
  4. statistics
  5. dollars
  6. percent
  7. calculations
  8. worst
  9. businesses
  10. institutions
  11. unethical
  12. place
  13. simplified
  14. seventy
  15. board
  16. argue
  17. annoyed
  18. practical
  19. million
  20. traveling
  21. penalties
  22. flights
  23. people
  24. optimize
  25. airline
  26. tickets
  27. version
  28. thousand
  29. probability
  30. practice

Original Text

Have you ever sat in a doctor's office for hours despite having an appointment at a specific time, has the hotel turned down your reservation because it's full? Or have you been bumped off a flight that you paid for? These are all symptoms of overbooking, a practice where businesses and institutions sell or book more than their full capacity. While often infuriating for the customer, overbooking happens because it increases profits while also letting businesses optimize their resources. They know that not everyone will show up to their appointments, reservations and flights, so they make more available than they actually have to offer. Airlines are the classical example, partially because it happens so often, about 50000 people get bumped off their flights each year. That figure comes at little surprise to the airlines themselves, which used statistics to determine exactly how many tickets to sell. It's a delicate operation, sell too few and they're wasting seats, sell too many and they pay penalties, money, free flights, hotel stays and annoyed customers. So here's a simplified version of how their calculations work. Airlines have collected years worth of information about who does and doesn't show up for certain flights. They know, for example, that on a particular route, the probability that each individual customer will show up on time is 90 percent. For the sake of simplicity, will assume that every customer is traveling individually rather than as families or groups, then if there are 180 seats on the plane and they sell 180 tickets, the most likely result is that 162 passengers will board. But of course, you could also end up with more passengers or fewer. The probability for each value is given by what's called a binomial distribution, which peaks at the most likely outcome. Now let's look at the revenue. The airline makes money from each ticket buyer and loses money for each person who gets bumped. Let's say a ticket costs 250 dollars and isn't exchangeable for a later flight and the cost of bumping a passenger is 800 dollars. These numbers are just for the sake of example. Actual amounts vary considerably. So here, if you don't sell any extra tickets, you make 45000 dollars. If you sell 15 extras and at least 15 people are no shows, you make forty eight thousand seven hundred fifty dollars. That's the best case. In the worst case, everyone shows up, 15 unlucky passengers get bumped and the revenue will only be thirty six thousand seven hundred fifty dollars, even less than if you only sold 180 tickets in the first place. But what matters isn't just how good or bad a scenario is financially, but how likely it is to happen. So how likely is each scenario? We can find out by using the binomial distribution in this example, the probability of exactly 195 passengers boarding is almost zero percent. The probability of exactly 184 passengers boarding is one point one one percent and so on. Multiply these probabilities by the revenue for each case, add them all up and subtract the sum from the earnings by 195 sold tickets and you get the expected revenue for selling 195 tickets. By repeating this calculation for various numbers of extra tickets, the airline can find the one likely to yield the highest revenue in this example. That's 198 tickets from which the airline will probably make forty eight thousand seven hundred seventy four dollars, almost 4000 more than without overbooking. And that's just for one flight. Multiply that by a million flights per airline per year. And overbooking adds up fast. Of course, the actual calculation is much more complicated airlines apply many factors to create even more accurate models, but should they? Some argue that overbooking is unethical. You're charging two people for the same resource. Of course, if you're 100 percent sure someone won't show up, it's fine to sell their seat. But what if you're only 95 percent sure, 75 percent. Is there a number that separates being unethical from being practical?

Frequently Occurring Word Combinations

ngrams of length 2

collocation frequency
passengers boarding 2

Important Words

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  2. actual
  3. add
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  5. airline
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  33. cost
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  38. delicate
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  40. distribution
  41. dollars
  42. earnings
  43. exchangeable
  44. expected
  45. extra
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  47. factors
  48. families
  49. fast
  50. fifty
  51. figure
  52. financially
  53. find
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  57. forty
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  70. infuriating
  71. institutions
  72. letting
  73. loses
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  78. multiply
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  112. route
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  128. statistics
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  130. subtract
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  135. ticket
  136. tickets
  137. time
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  139. turned
  140. unethical
  141. unlucky
  142. vary
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  148. year
  149. years
  150. yield